In [173]:
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
import time
import tqdm
from sklearn.svm import LinearSVC
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
from skimage.feature import hog
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle as sk_shuffle
import collections
from scipy.ndimage.measurements import label
In [174]:
# Read in car and non-car images
images = glob.glob('data_set/**/**/*.png')
cars = []
notcars = []
for image in images:
if 'non-' in image:
notcars.append(image)
else:
cars.append(image)
print(images[:10])
In [175]:
def color_hist(img, nbins=32,bins_range=(0, 256) ):
# Compute the histogram of the color channels separately
channel1_hist = np.histogram(img[:,:,0], bins=nbins)
channel2_hist = np.histogram(img[:,:,1], bins=nbins)
channel3_hist = np.histogram(img[:,:,2], bins=nbins)
# Concatenate the histograms into a single feature vector
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
# Return the individual histograms, bin_centers and feature vector
return hist_features
In [176]:
#convertin image to 1D using ravel#conver
def bin_spatial(img, color_space='RGB', size=(32, 32)):
# Convert image to new color space (if specified)
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
# Use cv2.resize().ravel() to create the feature vector
# features = cv2.resize(feature_image, size).ravel()
color1 = cv2.resize(feature_image[:,:,0], size).ravel()
color2 = cv2.resize(feature_image[:,:,1], size).ravel()
color3 = cv2.resize(feature_image[:,:,2], size).ravel()
return np.hstack((color1, color2, color3))
# Return the feature vector
return features
In [177]:
def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):
if vis == True:
# Use skimage.hog() to get both features and a visualization
features, hog_image = hog(img, orientations=orient,pixels_per_cell=(pix_per_cell, pix_per_cell), cells_per_block=(cell_per_block, cell_per_block), visualise=True, feature_vector=False)
return features, hog_image
else:
# Use skimage.hog() to get features only
features = hog(img, orientations=orient,pixels_per_cell=(pix_per_cell, pix_per_cell), cells_per_block=(cell_per_block, cell_per_block), visualise=False, feature_vector=feature_vec)
return features
In [178]:
# Define a function to extract features from a list of images
# Have this function call bin_spatial() and color_hist()
def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
# Create a list to append feature vectors to
features = []
# Iterate through the list of images
for file in imgs:
file_features = []
# Read in each one by one
image = mpimg.imread(file)
# apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
file_features.append(spatial_features)
if hist_feat == True:
# Apply color_hist()
hist_features = color_hist(feature_image, nbins=hist_bins)
file_features.append(hist_features)
if hog_feat == True:
# Call get_hog_features() with vis=False, feature_vec=True
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
# Append the new feature vector to the features list
file_features.append(hog_features)
features.append(np.concatenate(file_features))
# Return list of feature vectors
return features
In [179]:
color_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
spatial_size = (32, 32) # Spatial binning dimensions
hist_bins = 32 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
car_features = extract_features(cars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
notcar_features = extract_features(notcars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
In [180]:
# Create an array stack of feature vectors
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=rand_state)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X_train)
# Apply the scaler to X
X_train = X_scaler.transform(X_train)
X_test = X_scaler.transform(X_test)
In [181]:
print('Using:',orient,'orientations',pix_per_cell,
'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC
svc = LinearSVC()
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
In [182]:
def draw_boxes(img, bboxes, color=(255, 0, 0), thick=6):
imcopy = np.copy(img) # Make a copy of the image
for bbox in bboxes: # Iterate through the bounding boxes
cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
return imcopy
# Define a function that takes an image,
# start and stop positions in both x and y,
# window size (x and y dimensions),
# and overlap fraction (for both x and y)
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],
xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
# If x and/or y start/stop positions not defined, set to image size
if x_start_stop[0] == None:
x_start_stop[0] = 0
if x_start_stop[1] == None:
x_start_stop[1] = img.shape[1]
if y_start_stop[0] == None:
y_start_stop[0] = 0
if y_start_stop[1] == None:
y_start_stop[1] = img.shape[0]
# Compute the span of the region to be searched
xspan = x_start_stop[1] - x_start_stop[0]
yspan = y_start_stop[1] - y_start_stop[0]
# Compute the number of pixels per step in x/y
nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
# Compute the number of windows in x/y
nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step)
ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step)
# Initialize a list to append window positions to
window_list = []
# Loop through finding x and y window positions
# Note: you could vectorize this step, but in practice
# you'll be considering windows one by one with your
# classifier, so looping makes sense
for ys in range(ny_windows):
for xs in range(nx_windows):
# Calculate window position
startx = xs*nx_pix_per_step + x_start_stop[0]
endx = startx + xy_window[0]
starty = ys*ny_pix_per_step + y_start_stop[0]
endy = starty + xy_window[1]
# Append window position to list
window_list.append(((startx, starty), (endx, endy)))
# Return the list of windows
return window_list
car_images = glob.glob('test_images/test*.jpg')
img = mpimg.imread(car_images[0])
windows = slide_window(img, x_start_stop=[None, None], y_start_stop=[400, 700],
xy_window=(128, 128), xy_overlap=(0.5, 0.5))
window_img = draw_boxes(img, windows, color=(0, 0, 255), thick=6)
plt.imshow(window_img)
plt.show()
img = mpimg.imread(car_images[0])
windows = slide_window(img, x_start_stop=[None, None], y_start_stop=[400, 700],
xy_window=(96, 96), xy_overlap=(0.5, 0.5))
window_img = draw_boxes(img, windows, color=(0, 0, 255), thick=6)
plt.imshow(window_img)
plt.show()
In [183]:
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
#1) Define an empty list to receive features
img_features = []
#2) Apply color conversion if other than 'RGB'
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(img)
#3) Compute spatial features if flag is set
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
# print("spatial_features",spatial_features.shape)
#4) Append features to list
img_features.append(spatial_features)
#5) Compute histogram features if flag is set
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
# print("hist_features",hist_features.shape)
#6) Append features to list
img_features.append(hist_features)
#7) Compute HOG features if flag is set
if hog_feat == True:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.extend(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#8) Append features to list
img_features.append(hog_features)
# print("hog_features",hog_features.shape)
#9) Return concatenated array of features
return np.concatenate(img_features)
In [184]:
def search_windows (img, windows, svc, scaler, color_space='RGB',
spatial_size=(32, 32), hist_bins=32,
hist_range=(0, 256), orient=8,
pix_per_cell=8, cell_per_block=2,
hog_channel=0, spatial_feat=True,
hist_feat=True, hog_feat=True):
#1) Create an empty list to receive positive detection windows
on_windows = []
#2) Iterate over all windows in the list
for window in windows:
#3) Extract the test window from original image
test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))
#4) Extract features for that window using single_img_features()
features = single_img_features(test_img, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
#5) Scale extracted features to be fed to classifier
test_features = scaler.transform(np.array(features).reshape(1, -1))
# print('test_features',test_features.shape)
#6) Predict using your classifier
prediction = svc.predict(test_features)
#7) If positive (prediction == 1) then save the window
if prediction == 1:
on_windows.append(window)
#8) Return windows for positive detections
return on_windows
image = mpimg.imread('test_images/test6.jpg')
color_space = 'RGB' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
spatial_size = (32, 32) # Spatial binning dimensions
hist_bins = 32 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
color_spacecolor_sp = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9 # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
spatial_size = (32, 32) # Spatial binning dimensions
hist_bins = 32 # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
y_start_stop=[400, 592]
windows = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop,
xy_window=(96, 96), xy_overlap=(0.5, 0.5))
hot_windows = search_windows(image, windows, svc, X_scaler, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
window_img = draw_boxes(image, hot_windows, color=(255, 0, 0), thick=6)
plt.imshow(window_img)
plt.show()
In [185]:
t=time.time() # Start time
for image_p in glob.glob('test_images/test*.jpg'):
image = mpimg.imread(image_p)
draw_image = np.copy(image)
windows = slide_window(image, x_start_stop=[100, None], y_start_stop=[400, 640],
xy_window=(128, 128), xy_overlap=(0.5, 0.5))
# window_img = draw_boxes(draw_image, windows, color=(0, 0, 255), thick=6)
hot_windows = []
hot_windows += (search_windows(image, windows, svc, X_scaler, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat))
window_img = draw_boxes(draw_image, hot_windows, color=(255, 0, 0), thick=6)
plt.imshow(window_img)
plt.show()
# show_img(window_img)
# print(round(time.time()-t, 2), 'Seconds to process test images')
In [186]:
def convert_color(img, conv='RGB2YCrCb'):
if conv == 'RGB2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
if conv == 'BGR2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
if conv == 'RGB2LUV':
return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
if conv == 'RGB2YUV':
return cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
In [187]:
# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins):
draw_img = np.copy(img)
img = img.astype(np.float32)/255
bboxes = []
img_tosearch = img[ystart:ystop,:,:]
ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YCrCb')
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1
nfeat_per_block = orient*cell_per_block**2
# 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
window = 64
nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
cells_per_step = 2 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step + 1
nysteps = (nyblocks - nblocks_per_window) // cells_per_step + 1
# Compute individual channel HOG features for the entire image
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
for xb in range(nxsteps):
for yb in range(nysteps):
ypos = yb*cells_per_step
xpos = xb*cells_per_step
# Extract HOG for this patch
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
# Extract the image patch
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
# Get color features
spatial_features = bin_spatial(subimg, size=spatial_size)
hist_features = color_hist(subimg, nbins=hist_bins)
# Scale features and make a prediction
test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
#test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))
test_prediction = svc.predict(test_features)
if test_prediction == 1:
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
box = ((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart))
bboxes.append(box)
cv2.rectangle(draw_img,box[0],box[1],(0,0,255),6)
return draw_img, bboxes
image = mpimg.imread('test_images/test6.jpg')
for image_p in glob.glob('test_images/test*.jpg'):
image = mpimg.imread(image_p)
ystart = 350
ystop = 720
scale = 1.0
draw_img, bboxes = find_cars(image, ystart, ystop, scale, svc,
X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
fig = plt.figure()
plt.imshow(draw_img)
In [188]:
def add_heat(heatmap, bbox_list):
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
# Return updated heatmap
return heatmap# Iterate through list of bboxes
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
def draw_labeled_bboxes(img, labels):
# Iterate through all detected cars
for car_number in range(1, labels[1]+1):
# Find pixels with each car_number label value
nonzero = (labels[0] == car_number).nonzero()
# Identify x and y values of those pixels
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Define a bounding box based on min/max x and y
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
# Draw the box on the image
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
# Return the image
return img
image = mpimg.imread('test_images/test6.jpg')
for image_p in glob.glob('test_images/test*.jpg'):
image = mpimg.imread(image_p)
heat = np.zeros_like(image[:,:,0]).astype(np.float)
ystart = 350
ystop = 720
scale = 1.2
draw_img, bboxes = find_cars(image, ystart, ystop, scale, svc,
X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
# Add heat to each box in box list
heat = add_heat(heat,bboxes)
# Apply threshold to help remove false positives
heat = apply_threshold(heat,1)
# Visualize the heatmap when displaying
heatmap = np.clip(heat, 0, 255)
# Find final boxes from heatmap using label function
labels = label(heatmap)
draw_img = draw_labeled_bboxes(np.copy(image), labels)
fig = plt.figure()
plt.subplot(121)
plt.imshow(draw_img)
plt.title('Car Positions')
plt.subplot(122)
plt.imshow(heatmap, cmap='hot')
plt.title('Heat Map')
fig.tight_layout()
In [189]:
heatmaps = collections.deque(maxlen=7)
def _pipeline(img):
test_img=np.copy(img)
rects = []
ystart = 350
ystop = 464
scale = 1.0
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
ystart = 400
ystop = 480
scale = 1.0
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
ystart =400
ystop = 592
scale = 1.5
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
ystart = 400
ystop = 528
scale = 1.5
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
ystart = 400
ystop = 592
scale = 1.8
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
ystart = 400
ystop = 528
scale = 2.0
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
ystop = 560
scale = 1.8
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
ystop = 560
scale = 2.0
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
ystop = 596
scale = 3.5
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
ystart = 464
ystop = 660
scale = 3.5
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
rectangles = [a for rect in rects for a in rect]
return rectangles
def annotate_brake(img):
text = 'BRAKE'
cv2.putText(img, text, (40,120), cv2.FONT_HERSHEY_DUPLEX, 1.5, (200,255,155), 2, cv2.LINE_AA)
return img
def rear_end_pipeline(img):
test_img=np.copy(img)
rects = []
# Rear End Detection
ystart = 100
ystop = 660
scale = 6.5
draw_img,window_list = find_cars(test_img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
rects.append(window_list)
rectangles = [a for rect in rects for a in rect]
heatmap = _heatmap(img, rectangles)
draw_img = _labels(img, heatmap)
if len(rectangles) > 0:
annotate_brake(img)
return draw_img
def _heatmap(img, rectangles):
heat = np.zeros_like(image[:,:,0]).astype(np.float)
heat = add_heat(heat,rectangles)
# Apply threshold to help remove false positives
heat = apply_threshold(heat,1)
# Visualize the heatmap when displaying
heatmap = np.clip(heat, 0, 255)
return heatmap
def _labels(img, heatmap):
# Find final boxes from heatmap using label function
labels = label(heatmap)
draw_img = draw_labeled_bboxes(np.copy(img), labels)
return draw_img
def pipeline(img):
rectangles = _pipeline(img)
heatmap = _heatmap(img, rectangles)
draw_img = _labels(img, heatmap)
return draw_img
# def pipeline_heatmap(img):
# heat = np.zeros_like(image[:,:,0]).astype(np.float)
# rectangles = _pipeline(img)
# heat = add_heat(heat,rectangles)
# # Apply threshold to help remove false positives
# heat = apply_threshold(heat,1)
# # Visualize the heatmap when displaying
# heatmap = np.clip(heat, 0, 255)
# return heatmap
def pipeline_heatmap(img):
rectangles = _pipeline(img)
draw_img = _heatmap(img, rectangles)
return draw_img
In [198]:
fig, axs = plt.subplots(6,2, figsize=(10, 20))
axs = axs.ravel()
i = 0
for image_p in glob.glob('test_images/test*.jpg'):
img = mpimg.imread(image_p)
img_out = pipeline(img)
axs[i].imshow(img_out)
axs[i].axis('off')
i += 1
img_hm = pipeline_heatmap(img)
axs[i].imshow(img_hm)
axs[i].axis('off')
i += 1
In [105]:
from moviepy.editor import VideoFileClip
video_output1 = 'project_video_output.mp4'
video_input1 = VideoFileClip('project_video.mp4')
processed_video = video_input1.fl_image(pipeline)
%time processed_video.write_videofile(video_output1, audio=False)
In [113]:
from moviepy.editor import VideoFileClip
video_output1 = 'project_video_output_heatmap.mp4'
video_input1 = VideoFileClip('project_video.mp4')
processed_video = video_input1.fl_image(pipeline_heatmap)
%time processed_video.write_videofile(video_output1, audio=False)
In [195]:
fig, axs = plt.subplots(3,2, figsize=(10, 20))
axs = axs.ravel()
for i, image_p in enumerate(glob.glob('test_images/rear_end*.png')):
img = mpimg.imread(image_p)
img_out = rear_end_pipeline(img)
axs[i].imshow(img_out)
